Classifying Verb Phrase Semantics - Semantic Scholar

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Relationships are an essential part of the design of a database be- ... carried out in database design (Embley et al. ... as in Author writes Book.
Understanding Relationships: Classifying Verb Phrase Semantics* Veda C. Storey1 and Sandeep Purao2 1

J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30302 [email protected] 2 School of Information Sciences & Technology, The Pennsylvania State University, University Park, PA 16801-3857 [email protected]

Abstract. Relationships are an essential part of the design of a database because they capture associations between things. Comparing and integrating relationships from heterogeneous databases is a difficult problem, partly because of the nature of the relationship verb phrases. This research proposes a multilayered approach to classifying the semantics of relationship verb phrases to assist in the comparison of relationships. The first layer captures fundamental, primitive relationships based upon well-known work in data abstractions and conceptual modeling. The second layer captures the life cycle of natural progressions in the business world. The third layer reflects the context-dependent nature of relationships. Use of the classification scheme is illustrated by comparing relationships from various application domains with different purposes.

1 Introduction Comparing and integrating databases is an important problem, especially in an increasingly networked world that relies on inter-organizational coordination and systems. With this, is the need to develop new methods to design and integrate disparate databases. Database integration, however, is a difficult problem and one for which semi-automated approaches would be useful. One of the main difficulties is comparing relationships because their verb phrases may be generic or dependent upon the application domain. Being able to compare the semantics of verb phrases in relationships would greatly facilitate database design comparisons. It would be even more useful if the comparison process could be automated. Fully automated techniques, however, are unlikely so solutions to integration problems should aid integrators, but require minimal work on their part [Biskup and Embley, 2003]. The objective of this research is to: propose an ontology for understanding the semantics of relationship verb phrases by mapping the verb phrases to various categories that capture different interpretations. Doing so requires that a classification scheme be developed that captures both the domain-dependent and domain independent nature of verb phrases. *

This research was partially supported by J. Mack Robinson College of Business, Georgia State University and Pennsylvania State University.

The contribution of this research is to provide a useful approach to classifying verb phrases so relationships can be compared in a semi-automated way.

2 Related Work The design of a database involves representing the universe of discourse in a structure in such a way that it accurately reflects reality. Conceptual modeling of databases is, therefore, concerned with things (entities) and associations among things (relationships) [Chen 1993; Wand et al. 1999]. A relationship R, can be expressed as A verb phrase B (A vp B), where A and B are entities. Most database design practices use simple, binary associations that capture these relationships between entities. A verb phrase, which is selected by a designer with the application domain in mind, can capture much of the semantics of the relationship. Semantics, for this research, is defined as the meaning of a term or a mapping from the real world to a construct. Understanding a relationship, therefore, requires that one understand the semantics of the accompanying verb phrase. Consider the relationships from two databases: Customer (entity) buys (verb) Product (entity) Customer (entity) purchases (verb) Product (entity) These relationships reflect the same aspect of the universe of discourse, and use synonymous verb phrases. Therefore, the two relationships may be mapped to a similar interpretation, recognized as identical, and integrated. Next, consider: Customer reserves Car Customer rents Car. These relationships reflect different concepts from the universe of discourse. The first captures the fact that a customer wants to do something; the second, that the customer has done it. These may be viewed as different states in a life cycle progression, but the two underlying relationships cannot be considered identical. Thus, they could not be mapped to the same semantic interpretation. Finally, consider: Manager considers Agreement Manager negotiates Agreement. The structures of the relationships suggest that both relationships represent an interaction. However, “negotiates” implies changing the status, whereas “considers” involves simply viewing the status. On the other hand, Manager makes Agreement Manager writes Agreement may capture an identical notion of creation. These examples illustrate the importance of employing and understanding how a verb phrase captures the semantics of the application domain. The interpretation of verbs depends upon the nouns (entities) that surround them [Fellbaum, 1998]. Research has been carried out on defining and understanding ontology creation and use. There are different definitions and interpretations of ontologies [Weber 2002]. In general, though, ontologies deal with capturing, representing, and using surrogates for the meanings of terms. This research adopts the approach of Dahlgren [1988] who developed an ontology system as a classification scheme for speech understanding

and implemented it as an interactive tool. Work on ontology development has been carried out in database design (Embley et al. [1999], Kedad and Metais [1999], Dullea and Song [1999], Bergholtz and Johannesson [2001]). These efforts provide useful insights and build upon data abstractions. However, no comprehensive ontology for classifying relationships has been proposed.

3 Ontology for Classifying Relationships This section proposes an ontology for classifying the verb phrases of relationships. The ontology is of the type developed by Dahlgren [1988] which operates as an interactive system to classify things. The most important part is the classification scheme. It is the focus of this research and is divided into three layers (Figure 1). The layers were developed by considering: 1) prior research in data modeling, in particular, data abstractions and the inherent business life cycle; 2) the local context of the entities; and 3) the domain-dependent nature of verb phrases.

Fundamental Categories

Entity 2

Entity1

Local (internal) context

Domain Global (external) context

Relationship A verb phrase B Figure 1. Relationship classification levels 3.1 Fundamental Categories The fundamental categories are primitives that reflect a natural division in the real world. This category has three general classes that form the basis of how things in the real world can be associated with each other: status, change in status, and interaction as shown in Figure 2.

Status: the orientation of one entity towards another entity. e.g. A B Change of status: change of one entity with respect the other. e.g. A B. Interaction: communication or operation between entities that does not result in a change of status. e.g. A B. Figure 2. Fundamental Categories Status captures the fact that one thing has a status with respect to the other. These are primitive because they describe a permanent, or durable, association of one entity with another, expressing the fact that A with respect to B. Business applications follow a natural life cycle of conception or creation through to ownership and, eventually, destruction. The change of status category describes this transition from one status to another. Relationships in this category express the fact that A is transitioning from A with respect to B to A with respect to B. An interaction does not necessarily lead to a change of status of either entity. This happens when the effect of an interaction is worth remembering. Consider the verb phrase, ‘create.’ In some cases, it is useful to remember this as a status as in Author writes Book. In other cases, the interaction itself is important, even if it does not result in a change of status. The interaction category, therefore, expresses the fact that A with respect to B. hese fundamental categories are sufficiently coarse that all verb phrases will map to them. They are also coarse enough to warrant finer categories to distinguish among the large set of relationships in each category. Thus, further refinement is needed for each fundamental category. 3.1.1 Refining the Category: Status The ‘Status’ category has been extensively studied by research on data abstractions, which focuses on the structure of relationships as a surrogate for understanding their semantics. Most data abstractions associate entities at different levels of abstraction (sub/superclass relationships) [Goldstein and Storey, 1999]. Since data abstractions infer semantics based on the structure of relationships, they, thus, provide a good start point for understanding the semantics of relationships. Research on understanding natural language also provides verb phrase categories such as auxiliary, generic and other types. The first layer captures fundamental differences between kinds of relationships and was build by considering prior, well-accepted research on data abstractions and other frequently-used verb phrases whose interpretation is unambiguous. These are independent of context. This category, thus, captures the fundamental ways in which things in the real world are related so the categories in this level can be used to distinguish among the fundamental types. Additional results from research on patterns [Coad, 1995] and linguistic analysis [Miller, 1990] results in a hierarchical classifica-

tion with defined primitives at the leaves of the tree. Figure 3 shows this finer classification of the category ‘Status.’ Status

Structural

Influential

Temporal

Attitudinal*

Spatial

Is-a

Creator

Follow/Precede

Part-of

Destroyer

Require*

Member-of

Controller

Instance-of

Owner

Version-of

Assigned-to*

Descriptor-of

Subjected-to*

Legend: Leaf nodes (marked in grey) indicate status primitives, asterisk represent additions

Figure 3. Primitives for the Category ‘Status’ Examples of primitive status relationships are shown in Table 1. There are two variations of one thing being assigned to another: is-assigned-to and is-subjected-to. In A is-subjected-to B, A does not have a choice with respect to its relationship with B, whereas it might in the former. Temporal relationships capture the sequence of when things happen and can be clearly categorized as before, during, and after. Primitive

Example

Source

1

A is-a B

Pilot Employee

[Brachman 1983]

2

A is-member-of B

[Brodie 1981]

3

A is-part-of B

Professor Department Car Engine

4

A is-instance-of B

Video Tape Movie

5

A is-version-of B

Draft Manuscript

[Motsching-Pitrik and Mylopoulous 1992] [Motsching-Pitrik, 2000]

6

A is-descriptor-of B

Document Task

[Larmon 1997, p. 156]

7

A is-creator-of B

Author Book

8

A is-destroyer-of B

Tennant Lease

9

A is-owner-of B

Company Building

[Gamma et al. 1995, p. 87] [Gamma et al. 1995, p. 266] [Larmon 1997, p. 157]

[Smith and Smith 1977]

10

A is-in-control-of B

Manager Team

[Larmon 1997, p. 156]

11

A is-assigned-to B

Employee Project

Added

12

A is-subjected-to B

Industry Law

Added

13

A follows-or-precedes B

Rental Reservation

[Hay 1996, Chp. 5]

14

A requires B

15

A is-next-to B

16

A has-attitudetowards B

Construction Approval San Andreas Fault Los Angeles Customer Product

Added [Larmon 1997, p. 156; Hay 1996, p. 36] Added

Table 1: Primitives for ‘Status’ category

3.1.2 Refining the Category: Change of Status The change-of-status primitives, in conjunction with the status primitives, capture the lifecycle transitions for each status. Although the idea of a lifecycle has been alluded to previously [Hay 1996], prior research has not systematically recognized the lifecycle concept. Our conceptualization of the ‘Change of Status’ category is based on an extension and understanding of each primitive in the ‘Status’ category during the business lifecycle. Consider verb phrases that deal with acquiring something, as is typical of business transactions related to the status primitive ‘is-owner-of.’ The lifecycle for this status primitive has the states shown in Figure 4. attemptsto-bewants-tobe-owner-

becomesowner-of

Relationship Lifecycle for Status Primitive: ‘is-owner-of’

is-owner-of

ceases-tobe-ownerLegend: status primitive for which the lifecycle is portrayed marked in grey

attempts-togive-upownership-

dislikesbeing-

Figure 4. The Relationship Life Cycle Each state may, in turn, be mapped to different status primitives. For example, the lifecycle starts with needing something (‘has-attitude-towards’ and ‘requires’) which is followed by intending to become an owner (‘acquire’ or ‘create’), owning (‘owner’ or ‘in-control-of’) and giving up ownership (‘seller’ or ‘destroyer’). The primitives therefore illustrate a lifecycle that goes through creation or acquisition, ownership, and destruction. The life cycle can be logically divided into: intent, attempt to acquire, transition to acquiring, intent to give up, attempt to give up, and transition to giving up. Table 2 shows this additional information superimposed on the different

states within the lifecycle. The sub-column under the change-of-status primitives shows the meanings captured in each: intent, attempt and the actual transition.

Primitive

Example

A wants-to-be B intent Customer Product A attempts-to-become attempt Customer Product owner of B A becomes B transition Customer Product Status Primitive: Customer Product A dislikes-being B intent Company Product A attempts-to-give-up B attempt Company Product A gives-up ownership-of B transition Company Product Table 2: Primitives for the Category ‘Change of Status’

3.1.3 Refining the Category: Interaction ‘Interaction’ describes communication of short duration between two entities or an operation of one entity on another. The interaction may cause a change in one of the entities. For example, one entity may ‘manipulate’ another [Miller, 1990], or cause movement of the other through time or space (‘transmit,’ ‘receive’). Two entities may interact without causing change to either (‘communicate with,’ ‘observe’). One entity may interact with another also by way of performance (‘operate,’ ‘serve’). Figure 5 shows the primitives for ‘Interaction’ with examples given in Table 3. Interaction

Not Causing Change

Observe

Performance

Communicate

View Status

Select

Causing Change

Perform

Manipulate

Transfer

Operate

Serve

Transmit

Leaf nodes (grey) are primitives.

Figure 5. Primitives for the Category ‘Interaction’

Primitive

Example

Receive

1 2 3 4 5 6 7 8 9

View Status Select Communicate Perform Operate Serve Manipulate Transmit Receive

Analyst Requirements Customer Product Modem Phone Line Developer Software Pilot Plane Employee Customer Instructor Exam Bank Payment Warehouse Shipment

Table 3. Primitives for the Category ‘Interaction’ 3.2

The Local (Internal) Context

The second category captures internal context by taking into account the nature of the entities surrounding the verb phrase, highlighting the need to understand the nouns that surround verb phrases [Fellbaum, 1998]. For this research, entities are classified as: actor, action, and artifact. Actor entities are capable of performing independent actions. Action represents the performance of an act. Artifact represents an inanimate object not capable of independent action. After entities have been classified, valid primitives can be specified for each pair of entity types. For example, it does not make sense to allow the primitive ‘perform’ for two entities of the kind ‘Actor.’ On the other hand, this primitive is appropriate when one of the entities is classified as ‘Actor’ and the other as ‘Action.’ The argument can be applied both to the ‘Status’ and ‘Interaction’ primitives. Because the ‘Change of Status’ primitives capture the lifecycle of ‘Status’ primitives, constraints identified for ‘Status’ primitives apply to the ‘Change of Status’ primitives as well. Table 4 shows these constraints for ‘Status’ primitives. Similar constraints have been developed for the ‘Interaction’ primitives.

Entity 1 Actor Actor Actor

Entity 2 Actor Action Artifact

Action

Action

Action

Artifact

Artifact

Artifact

Valid Status Primitives control, creates/destructor, attitude, sequence, structure control, creates/destructor, attitude, not causing change control, creates/destructor, causing change, not causing change, transfer, exchange control, creates/destructor, attitude, sequence, not causing change, 3E control, creates/destructor, structure, causing change, not causing change control, creates/destructor, structure, causing change, not causing change, transfer, exchange

Table 4. Valid ‘Status’ Primitives based on Entity Context

3.3

Global (External) Context

The third level captures the external context, that is, the domain in which the relationship is used, reflecting the domain-dependent nature of verb phrases. Although attempts have been made to capture taxonomies of such domain-dependent verbs, a great deal of manual effort has been involved. This research takes a more pragmatic approach where a knowledge base of domain-dependent verb phrases may be constructed over time when the implemented ontology is being used. When the user classifies a verb phrase, its classification and application domain should be stored. Consider the use of ‘opens’ in a theatre database versus a bank database. The relationship Character Door in the theatre domain maps to the interaction primitive . In the bank application, Teller Account maps to the status primitive ; Customer Account maps to . If a verb phrase has already been classified by a user, it can be suggested as a preliminary classification for additional users, who are interested in classifying it. If a verb phrase has already been classified by a different user for the same application domain, then that classification should be displayed to the user who would agree with the classification or provide a new classification. New classifications will also be stored. Ideally, consensus will occur over time. This way the knowledge base builds up, ensuring that the verbs important to different domains are captured appropriately. The following will be stored: [Relationship, Verb phrase classification, Application Domain, User] 3.4

Use of the Ontology

The ontology can be used for comparing relationships across two databases by first comparing the entities, followed by classification of the verb phrases accompanying the relationships. . Examples are shown in Table 5. Relationship 1

Relationship 2

Contractor builds Bridge (Actor--Artifact)

Builder Constructs Tree house (Actor--Artifact)

Contractor builds Bridge (Actor--Artifact)

Contractor has Employee (Actor--Actor)

Contract builds Bridge (Actor--Artifact) Manager has Employee (Actor--Actor)

Worker does Raking (Actor--Action) Manager employs Worker (Actor--Actor)

Manager gets Employee (Actor--Actor)

Manager gets Contract (Actor--Artifact)

Employee finds Apprentice

Employ does Allocation

Relationship Comparison Entities similar; compare verb phrases Entities differ, do not compare verb phrases) Relationships differ Entities similar; compare verb phrases Entities differ; do not compare verb phrases Entities differ; do

(Actor--Actor)

(Actor--Action)

not compare verb phrases

Table 5. Relationship comparison considering classification of entities The ontology consists of a verb phrase classification scheme, a knowledge base that stores the classified verb phrases, organized by user and application, and a userquestioning scheme as mentioned above. The user is instructed to classify the entities of a relationship as actor, action, or artifact. The next step is to classify the verb phrase. First, the user is asked to select one the three categories: ‘Status,’ ‘Interaction,’ or ‘Change of Status.’ Based on this selection, and the constraints provided by the entity types, primitives within each category are presented to the user for an appropriate classification. Suppose a user classifies a relationship as ‘Status.’ Then, knowing the nature of the entities, only certain primitives are presented as possible for the classification of the relationship. Furthermore, identifying that a verb phase is either status, change or status, or interaction restricts the subset of categories from which an appropriate classification can be obtained and, hence, the options presented to the user. If the verb phrase cannot be classified in this way, then, the other levels are checked to see if they are needed.

4.

Assessment

Assessing an ontology is a difficult task. A plausible approach to assessment of an ontology is suggested by Gruninger and Fox [1995]. They suggest evaluating the ‘competency’ of an ontology. One of the ways to determine this ‘competency’ is to identify a list of queries that a knowledge-base, which builds on the ontology, should be able to answer (competency queries). Based on these queries, the ontology may be evaluated by posing questions such as: Does the ontology contain enough information to answer these types of queries? Do the answers require a particular level of detail or representation of a particular area? Noy and McGuiness [2001] suggest that the competency questions may be representative, but need not be exhaustive. Following our intent of classifying relationships for the purpose of comparison across databases, we attempted to assess whether the classification scheme of the ontology can provide a correct and complete classification of relationship verb phrases. To do so, a study was carried out which involved the following steps: 1) generation of the verb phrases to be classified; 2) generation of relationships using the verb phrases in different application domains; and 3) classification of all verb phrases. Step 1: Generation of Verb phrase Only business-related verbs were used because the intent of the relationship ontology is use for business databases. Furthermore, it restricts the scope of the research. Since the SPEDE verbs [Cottam, 2000] were developed for business applications, these automatically became part of the sample set. The researchers independently selected business-related verbs from a set of 700 generated randomly from WordNet.

The verbs that were common to the selections made by both researchers were added to the list from SPEDE. The same procedure was carried out from a set of 300 verbs that were randomly selected by people who support the online dictionary http://dictionary.cambridge.org/. This resulted in a total of 211 business verbs. Step 2: Generation of Relationships Containing Verbs by Application Domain For each verb, a definition was obtained from the on-line dictionary. Dictionaries provide examples for understanding and context, which helped to generate the relationships. Relationships were generated for seven application domains (approximately 30 verbs in each): 1) education, 2) business management, 3) manufacturing, 4) airline, 5) service, 6) marketing, 7and ) retail. Examples are shown in Table 6. Verb phrase Import

Source

Meaning(s)

Domain

SPEDE

to buy or bring in (products) from another country

Manufacturing

Obtain

SPEDE

Education

Collect

SPEDE

Accept

SPEDE

to get (something), esp. by asking for it, buying it, working for it or producing it from something else to gather together from a variety of places or over time to agree to take (something), or to take (something) as satisfactory, reasonable, true

Hire

SPEDE

to pay to use (something) for a short period or to pay (someone) to do a job temporarily

Service

Airline Retail

Generated Example Manufactures import Cars Students obtain Degrees Agent collects Ticket Supermarkets accept Credit Cards Travelers hire Cars

Table 6. Sample test relationships After generating the relationships, the researchers independently classified them using the relationship ontology. First, 30 verbs were classified and the researches agreed on 80% of the cases. The remaining verbs were then classified. The next step involved assessing how many of the ontology classifications the set of 211 verbs covered to test for completeness. The researchers generated additional relationships for ten subclasses for a total of 225. Sample classifications are shown in Table 7. Entity 1 Manufacturer Student Air traffic controller Salesperson

Verb Phrase Imports acquires establishes

Entity 2 Part Textbook Flight path

Classification receives becomes-owner-of becomes-creator-of

converts

Competitorcustomer

manipulates

Customer Caterer Teacher Airline

enters-into Delivers-to distributes Adjusts

Sales agreement Plane Handout Schedule

becomes-subjected-to is-assigned-to sends manipulates

Table 7: Sample classifications of relationships The results of this exercise were encouraging, especially given our focus on evaluating the competency of the ontology [Gruninger and Fox 1995]. The classification scheme worked well for these sample relationships. It allowed for the classification of all verb phrases. The biggest difficulty was in identifying whether to move from one level to the next. For example, Student acquires Textbook is immediately classifiable by the primitives. In other cases, the next layer was necessary. Further research is needed to design a user interface that can explain the use and categories to the user so they can be effectively applied. A preliminary version of a prototype has been developed. This will be completed and an empirical test carried out with typical end-users, most likely, database designers.

5 Conclusion A classification scheme for comparing relationship verb phrases has been presented. It is based upon results obtained from research on conceptual modeling, common sense knowledge of a typical life cycle, and the domain-dependent nature of relationships. Further research is needed to complete the ontology system for which the classification scheme will be a part. Then, it needs to be expanded to allow for multiple classifications and the user interface refined.

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